
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Any:







				"""simple docstring"""



				def get_masked_lm_array(a_		):
								__A       =					F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)

								if "kernel" in name:
												__A       =					array.transpose()

								return torch.from_numpy(a_		)

				def get_encoder_array(a_		):
								__A       =					F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)

								if "kernel" in name:
												__A       =					array.transpose()

								return torch.from_numpy(a_		)

				def get_encoder_layer_array(a_						,  a_		):
								__A       =					F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)

								if "kernel" in name:
												__A       =					array.transpose()

								return torch.from_numpy(a_		)

				def get_encoder_attention_layer_array(a_						,  a_						,  a_		):
								__A       =					F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)
								__A       =					array.reshape(a_		)

								if "kernel" in name:
												__A       =					array.transpose()

								return torch.from_numpy(a_		)

				print(F'''Loading model based on config from {config_path}...'''		)
				__A       =					BertConfig.from_json_file(a_		)
				__A       =					BertForMaskedLM(a_		)

				# Layers
				for layer_index in range(0						,  config.num_hidden_layers		):
								__A       =					model.bert.encoder.layer[layer_index]

								# Self-attention
								__A       =					layer.attention.self

								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_query_dense/kernel"						,  self_attn.query.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_query_dense/bias"						,  self_attn.query.bias.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_key_dense/kernel"						,  self_attn.key.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_key_dense/bias"						,  self_attn.key.bias.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_value_dense/kernel"						,  self_attn.value.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_value_dense/bias"						,  self_attn.value.bias.data.shape		)

								# Self-attention Output
								__A       =					layer.attention.output

								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_output_dense/kernel"						,  self_output.dense.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_output_dense/bias"						,  self_output.dense.bias.data.shape		)

								__A       =					get_encoder_layer_array(a_						,  "_attention_layer_norm/gamma"		)
								__A       =					get_encoder_layer_array(a_						,  "_attention_layer_norm/beta"		)

								# Intermediate
								__A       =					layer.intermediate

								__A       =					get_encoder_layer_array(a_						,  "_intermediate_dense/kernel"		)
								__A       =					get_encoder_layer_array(a_						,  "_intermediate_dense/bias"		)

								# Output
								__A       =					layer.output

								__A       =					get_encoder_layer_array(a_						,  "_output_dense/kernel"		)
								__A       =					get_encoder_layer_array(a_						,  "_output_dense/bias"		)

								__A       =					get_encoder_layer_array(a_						,  "_output_layer_norm/gamma"		)
								__A       =					get_encoder_layer_array(a_						,  "_output_layer_norm/beta"		)

				# Embeddings
				__A       =					get_encoder_array("_position_embedding_layer/embeddings"		)
				__A       =					get_encoder_array("_type_embedding_layer/embeddings"		)
				__A       =					get_encoder_array("_embedding_norm_layer/gamma"		)
				__A       =					get_encoder_array("_embedding_norm_layer/beta"		)

				# LM Head
				__A       =					model.cls.predictions.transform

				__A       =					get_masked_lm_array("dense/kernel"		)
				__A       =					get_masked_lm_array("dense/bias"		)

				__A       =					get_masked_lm_array("layer_norm/gamma"		)
				__A       =					get_masked_lm_array("layer_norm/beta"		)

				__A       =					get_masked_lm_array("embedding_table"		)

				# Pooling
				__A       =					BertPooler(config=a_		)
				__A       =					get_encoder_array("_pooler_layer/kernel"		)
				__A       =					get_encoder_array("_pooler_layer/bias"		)

				# Export final model
				model.save_pretrained(a_		)

				# Integration test - should load without any errors ;)
				__A       =					BertForMaskedLM.from_pretrained(a_		)
				print(new_model.eval()		)

				print("Model conversion was done sucessfully!"		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:int									=							argparse.ArgumentParser()
					parser.add_argument(
					    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
					)
					parser.add_argument(
					    '--bert_config_file',
					    type=str,
					    required=True,
					    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
					)
					parser.add_argument(
					    '--pytorch_dump_path',
					    type=str,
					    required=True,
					    help='Path to the output PyTorch model.',
					)
					SCREAMING_SNAKE_CASE				:Tuple									=							parser.parse_args()
					convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )